Publications

No Triangulation Without Representation: Generalization in Topological Deep Learning
Johannes S. Schmidt
Martin Carrasco
Ernst Röell
Guy Wolf
Nello Blaser
Bastian Rieck
Despite an ever-increasing interest in topological deep learning models that target higher-order datasets, there is no consensus on how to e… (see more)valuate such models. This is exacerbated by the fact that topological objects permit operations, such as structural refinements, that are not appropriate for graph data. In this work, we extend MANTRA, a benchmark dataset containing manifold triangulations, to a larger class of manifolds with more diverse homeomorphism types. We show that, unlike prior claims, both graph neural networks (GNNs) and higher-order message passing (HOMP) methods can saturate the benchmark. However, we find that this is contingent on the right representation and feature assignment, emphasizing their importance in baseline models. We thus provide a novel evaluation protocol based on representational diversity and triangulation refinement. Surprisingly, we find no indication that existing models are capable of generalizing beyond the combinatorial structure of the data. This points towards a research gap in developing models that understand topological structure independent of scale. Our work thus provides the necessary scaffolding to evaluate future models and enable the development of topology-aware inductive biases.
2026-05-06
arXiv (preprint)
doi.org
arxiv.org
Copy number variants reveal divergent genetic and diagnostic cortical signatures across psychiatric disorders
Kuldeep Kumar
Zhijie Liao
Jakub Kopal
Clara Moreau
Christopher Ching
Claudia Modenato
Will Snyder
Sayeh Kazem
Charles-Olivier Martin
Anne-Marie Bélanger
Valerie Fontaine
Khadije Jizi
Guillaume Huguet
Rune Boen
Leila Kushan
Ana Silva
Marianne van den Bree
David Linden
Michael Owen
Jeremy Hall … (see 14 more)
Sarah Lippé
Guillaume Dumas
Bodgan Draganski
Laura Almasy
Sophia Thomopoulos
Neda Jahanshad
Ida Sønderby
Ole Andreassen
David Glahn
Armin Raznahan
Carrie Bearden
Tomáš Paus
Paul Thompson
Sébastien Jacquemont
2026-05-05
Research Square (accepted)
doi.org
Decision Problems in Multilevel Linear Programming
Nagisa Sugishita
Margarida Carvalho
We study the computational complexity of decision problems in …
2026-05-05
arXiv (preprint)
doi.org
arxiv.org
Dissecting and steering cell dynamics using spatially-informed RNA velocity with veloAgent
Vishvak Raghavan
Brent Yoon
Gregory J Fonseca
Yue Li
Jun Ding
RNA velocity enables inference of cell state transitions from single-cell transcriptomics by modeling transcriptional dynamics from spliced … (see more)and unspliced mRNA. However, existing methods overlook spatial context and struggle to scale to large datasets, limiting insights into tissue organization and dynamic processes. We introduce veloAgent, a deep generative and agent-based framework that estimates gene- and cell-specific transcriptional kinetics while integrating spatial information through agent-based simulations of local microenvironments. By leveraging both molecular and spatial cues, veloAgent improves velocity accuracy and achieves sublinear memory scaling, enabling efficient analysis of large and multi-batch spatial datasets. A distinctive feature of veloAgent is its in silico perturbation module, which allows targeted manipulation of spatial velocity vectors to simulate regulatory interventions and predict their impact on cell fate dynamics. These capabilities position veloAgent as a scalable and versatile framework for dissecting spatially resolved cellular dynamics and guiding cell fate manipulation across diverse biological processes.
2026-05-05
Molecular Systems Biology (published)
doi.org
The utility of herbarium collections for genetic monitoring
Isaac Eckert
Lucas Eckert
Olivia Rahn
Cameron So
Simon Joly
Laura J. Pollock
Abstract Despite growing evidence of widespread genetic responses to anthropogenic activity, data shortfalls constrain genetic monitoring ef… (see more)forts and preclude the widespread use of genetic data to inform conservation. For flora, one option is to leverage the wealth of genetic material preserved in Earth’s vast herbarium collections, but the extent to which herbarium specimens can supply the population-level data required to monitor genetic change remains unclear. Using the Essential Biodiversity Variable (EBV) framework developed to monitor population-level genetic change, we show that digitized herbarium specimens could be used to quantify ∼162 K measures of genetic EBVs representing over 41 K species, 86% of regions on Earth, and spanning the past 250 years of global change. As such, we find that herbarium collections offer an invaluable source of historical genetic data, the mobilization of which could transform global efforts to monitor and conserve plant diversity.
2026-05-05
BioScience (published)
doi.org
Exploring Entropy-based Active Learning for Fair Brain Segmentation
Ghazal Danaee
Melanie Gaillochet
Christian Desrosiers
Hervé Lombaert
Sylvain Bouix
Active learning (AL) has emerged as a crucial strategy for reducing the prohibitive costs associated with medical image segmentation. Howeve… (see more)r, standard uncertainty-based AL methods typically focus on maximizing performance metrics, ignoring performance disparities or fairness across groups with sensitive attributes. While fair active learning has been explored in classification tasks, its intersection with medical image segmentation remains unaddressed. In this work, we introduced a fairness-aware active learning framework with a Weighted Entropy selection strategy that modulates uncertainty based on current group-specific performance estimates on the labeled set. To decouple true epistemic uncertainty from anatomical volume variances, we further utilized a masked, scaled entropy restricted to the region of interest. The framework was evaluated on synthetic T1-weighted brain MRIs with controlled left caudate bias in both strong and weak bias settings. A 3D U-Net was trained to segment the left caudate under several AL strategies, starting from both demographically balanced and strongly imbalanced initial labeled sets. Experiments demonstrated that our method markedly reduces performance disparities between groups compared to random sampling and standard uncertainty sampling. By prioritizing poorly segmented subgroups during the AL cycles, our method consistently achieved the highest equity-scaled performance and reduced the disparity metric by 75% (strong bias) and 86% (weak bias) relative to standard entropy at the final budget. Overall, this work is among the first studies on fair AL for medical image segmentation, offering an efficient strategy to train more equitable models in resource-constrained environments.
2026-05-04
Medical Imaging with Deep Learning (published)
doi.org
proceedings.mlr.press
One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation
Nathan Molinier
Hendrik Möller
Thomas Dagonneau
Anna Curto-Vilalta
Robert Graf
Matan Atad
Daniel Rueckert
Jan S. Kirschke
Julien Cohen-Adad
Deep learning-based medical image segmentation is increasingly used to support clinical diagnosis and develop new treatment strategies. Howe… (see more)ver, model performance remains limited by the scarcity of high-quality annotated data and insufficient generalization across imaging protocols. This limitation is particularly evident in MRI and CT, where models are typically trained on a single acquisition sequence and exhibit reduced robustness when applied to unseen sequences or contrasts. Although data augmentation is widely used to improve general robustness on medical images, its impact on cross-modality generalization has not been quantitatively explored. In this work, we study a targeted set of data augmentation techniques designed to improve cross-modality transfer. We train three spine segmentation models, each on a single-modality/sequence dataset, and evaluate them across seven out-of-distribution datasets (spanning CT and MRI), reflecting a realistic single-sequence training and multi-sequence/contrast/modality deployment scenario. Our results demonstrate substantial performance gains on unseen domains (average Dice gain of 155 %) while preserving in-domain accuracy (average Dice decrease of 0.008 %), including effective transfer between CT and MRI. To mitigate the computational cost typically associated with strong data augmentation, we implement GPU-optimized augmentations that maintain, and even improve, training efficiency by approximately 10 %. We release our approach as an open-source toolbox, enabling seamless integration into commonly used frameworks such as nnUNet and MONAI. These augmentations significantly enhance robustness to heterogeneous clinical imaging scenarios without compromising training speed.
2026-05-03
arXiv (preprint)
doi.org
arxiv.org
Cycles upon cycles - Temperature Scaling of Medaka Development
Sapna Chhabra
Victoria Mochulska
Carina B. Vibe
Anubhuti Anushree
Kristina S. Stapornwongkul
Thomas Thumberger
Joachim Wittbrodt
Paul François
Alexander Aulehla
ABSTRACT How organisms develop in dynamic environmental conditions is a fundamental question. We asked how day-night temperature cycles impa… (see more)ct embryonic axis elongation and segmentation, itself a cyclic process linked to the segmentation clock, using the Japanese rice fish medaka. We developed an unbiased dimensional reduction approach, based on Singular Value Decomposition (SVD), to reliably identify the dynamic modes of segmentation clock oscillations across all temperature conditions. We reveal that the two major dynamic modes show opposite temperature sensitivities: while the temporal oscillation (mode 1) varies strongly with temperature, the spatial phase gradient (mode 2) appears largely temperature invariant. In addition, we found developmental parameters with intermediate, sub-scaled temperature responses, such as axis elongation. We used theoretical modeling to understand how dynamic modes emerge from the underlying local oscillation dynamics and axis elongation. We then exposed embryos to circadian and ultradian temperature cycles to reveal dynamic response patterns of oscillations and axis elongation, and found how these responses are integrated into morphological features. Combined, our theoretical-experimental results support a model in which the dynamic integration of temporal (i.e. segmentation clock related) and spatial (i.e. axis elongation) processes, in particular their sub-scaled temperature response patterns, quantitatively compensate each other to yield a robust, temperature-invariant axis patterning outcome.
2026-05-01
bioRxiv (preprint)
doi.org
Segmentation of spinal rootlets across MRI contrasts with RootletSeg.
Katerina Krejci
Jiri Chmelik
Sandrine Bédard
Falk Eippert
Ulrike Horn
Virginie Callot
Julien Cohen-Adad
Jan Valosek
Segmentation of spinal nerve rootlets is relevant for spinal level estimation, lesion classification, neuromodulation therapy, and group-lev… (see more)el analyses. The aim of this study was to develop a deep learning method for the automatic segmentation of C2-T1 dorsal and ventral spinal nerve rootlets on various MRI scans. The study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T turbo spin echo T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years ± 6.53 [SD]; 28 [56%] males, 22 [44%] females) and achieved a mean ± SD Dice score of 0.67 ± 0.09 for T1w-INV2, 0.65 ± 0.11 for UNIT1, 0.64 ± 0.08 for T2w, and 0.62 ± 0.10 for T1w-INV1 contrasts. RootletSeg accurately segmented C2-T1 spinal rootlets across MRI contrasts, enabling the determination of spinal levels directly from MRI scans. The method is open-source and can be used for a variety of downstream analyses.
2026-05-01
Scientific Reports (published)
doi.org
Automatic multiple sclerosis lesion segmentation in the spinal cord using 3 T and 7 T MP2RAGE images
Nilser Laines Medina
Samira Mchinda
Benoit Testud
Sarah Demortière
Emanuele Pravatà
Govind Nair
Daniel S. Reich
Cristina Granziera
Charidimos Tsagkas
Virginie Callot
Julien Cohen-Adad
2026-04-30
Multiple Sclerosis and Related Disorders (published)
doi.org
ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks
Ta Thanh Thuy
Jiaqi Zhu
Xuan Liu
Lin Shang
Reihaneh Rabbany
Guillaume Rabusseau
Lihui Chen
Zheng Yilun
Sitao Luan
Understanding how people argue across ideological divides online is important for studying political polarization, misinformation, and conte… (see more)nt moderation. Existing datasets capture only part of this problem: some preserve text but ignore interaction structure, some model structure without rich semantics, and others represent conversations without stable user-level ideological identity. We introduce ControBench, a benchmark for controversial discourse analysis that combines heterogeneous social interaction graphs with rich textual semantics. Built from Reddit discussions on three topics, Trump, abortion, and religion, ControBench contains 7,370 users, 1,783 posts, and 26,525 interactions. The graph contains user and post nodes connected by semantically enriched edges; in particular, user-comment-user edges encode both a reply and the parent comment that it responds to, preserving local argumentative context. User labels are derived from self-declared Reddit flairs, providing a scalable proxy for ideological identity without manual annotation. The resulting datasets exhibit low or negative adjusted homophily (Trump: -0.77, Abortion: 0.06, Religion: 0.04), reflecting the cross-cutting structure of real-world debate. We evaluate graph neural networks, pretrained language models, and large language models on ControBench and observe distinct performance patterns across topics and model families, especially when ideological boundaries are ambiguous. These results position ControBench as a challenging and realistic benchmark for controversial discourse analysis.
2026-04-30
arXiv (preprint)
doi.org
arxiv.org
Oscillatory co-expression of HES1 and HES5 Enables a hybrid state in a cross-repressive transcription factor regulatory motif.
Veronica Biga
Anzy Miller
Anoushka Kamath
Robert Lea
Ying Q P Mak
Antony Adamson
Elli Marinopoulou
Paul François
Nancy Papalopulu
Cerys Manning
Many cell fate decisions in the developing neural tube are directed by cross-repressive transcription factor (TF) motifs that generate bista… (see more)bility, such that cells express one TF but not both. Hybrid states in which cells express both cross-repressing fate determinants have been observed, but how these arise or persist remains unclear. Here, we focus on HES1 and HES5, auto-repressive, oscillatory TFs that regulate neural progenitor maintenance and are expressed in adjacent dorsoventral progenitor domains in the developing spinal cord. Knockdown experiments demonstrate that HES1 and HES5 are cross-repressing in mouse spinal cord neural progenitors, and live-cell imaging in vitro shows that they can be co-expressed, defining a hybrid state. In this state, HES co-oscillate in-phase within single cells. Computational modelling indicates that modulation of cross-repression strength or relative TF abundance destabilises this state, driving resolution towards a single oscillatory HES TF. This is consistent with in vivo analysis showing transient HES1/HES5 co-expression followed by progressive restriction to a single TF oscillator. Our findings suggest that oscillatory expression enables co-existence of cross-repressing TFs, allowing hybrid states within a developmental bistable motif.
2026-04-30
Development (published)
doi.org